The results of the system depend on the initial MT and ASR quality.

our results show that a minimal amount of information is sufficient to improve the translation significantly.


more specifically, we asked them to annotate cases that are not: • Accurate, i.e.


If the data used for the MT system training and the sentences we are going to decode belong to different domains, there will be little intersection between the MT system's phrase table and the decoded sentences.

this is not a trivial task as several alternatives can be used to define a phrase, but in our case the segmentation needs to be connected to the errors in the translation.


Additionally, we introduced a new dataset that we created for assessing our automatic strategies against.

contrary to the word-level QE, for which the segmentation boundaries are self-defined and clear, QE at phrase-level implies that one needs to delimit sub-segments within the segment.


We add to this process then defining the final "OK" and "BAD" phrases as sequences of adjacent "OK" and "BAD" labels.

phrase-level QE implies an intrinsic challenge: how to segment a machine translation into sequence of words (contiguous or not) that represent an error.


A master template of the original subtitles in English was provided by the film maker.

although human error annotation and evaluation are very time-consuming tasks, we have shown in Section 4. that this kind of data can provide interesting insights on the nature of human and machine translation in general, and subtitling in particular.


The corpus was compiled as part of a course on subtitling targeted at students enrolled in the BA Translation Studies programme at Saarland University.


In this step, term candidates that pass the pre-filter are scored and ranked.

the null hypothesis that we expect to reject is that 'occurrence of a candidate is independent from occurrence of frequent Chi-Square (Matsuo and Ishizuka, 2003)  Rapid Keyword Extraction (Rose et al., 2010) Weirdness Weirdness (Ahmad et al., 1999) GlossEx Glossry Extraction (Park et al., 2002) termEx term Extraction (Sclano and Velardi, 2007) terms'.


Although this is just an approximate estimate 8 , it can give an indication of the rarity of synaesthesia in everyday language.

for our pilot study, we experimented and compared two different methods for the (semi-)automatic extraction of synaesthesia.


For example, the English verb seem is used as an epistemic verb far more often than as a perception verb.

in terms of frequency the directionality generalisation is confirmed: most transfers go from the lower to the higher modalities in both English (62%) and Italian (74%) (see Strik Lievers, 2015a for a discussion on directionality).


No loops are allowed, which means that the rewriting process cannot yield to a symbol that appeared on the left hand-side of an applied rule.

note however, that a sequence of tokens matched with nAP may contain tokens for which the agreement test is not applicable, e.g.


We can see that we cover most combinations of modifiers and heads with ambiguities between 1 (monosemous) and 7.

we created a gold standard with a focus on two-part noun-noun compounds including • compounds and constituents from various frequency ranges; • compounds and constituents from various productivity ranges; • compounds and constituents with various numbers of senses; and • compounds with various semantic relations.


5 End users will also want to know if the LR is available for research, commercial use, etc.

although most of the LRs gathered in our survey included information about their availability for research (only 8 showed 'Unknown' for availability, cf.


6 Of the compiled LRs, 13 can be classified as monolingual lists.

upon closer inspection, two had to be moved to the 'Lexical resources' type.


This resource can be used for large scale language acquisition studies of how MWEs feature in child language.

we investigated the use of MwEs in naturalistic language acquisition data.


This is consistent with the aim of the present work, where we would like to evaluate terms without considering the context in which they occur.

english, lay: the corpus provided by the CLeF eHealth 2015 information retrieval shared task 3 (50M words).


(2009), who also use very coarsegrained semantic categories of the type 'goodness', 'heaviness', 'measures', etc.

this is done over a model trained with word2vec 5 on a 2014 dump of the Spanish Wikipedia, preprocessed and lemmatized with Freeling (Atserias et al., 2006).


Again the NP or PP that goes with the verb is annotated with CVC, and both this node and the verb must be children of the same VP constituent.

(12) The company has shut the power plant down.


The annotation process involved three annotators in each languages and the validation of MWEs is done using a majority vote decision.

we would like to thank Jaya Saraswati, Rajita Shukla, Laxmi Kashyap, Nilesh Joshi and Irawati Kulkarni from CFILT lab at IIT Bombay for giving their valuable contribution in gold standard data creation.


The quality of such approaches depends on the use of algorithms and also on the quality of resources used.

for LVCs, we extracted candidates of patterns noun followed by verb, adjective followed by verb, adverb followed by verb and verb followed by verb.


It remains difficult to explore or compare the syntactic characteristics of various languages using the complete grammar (independently of the formalism, constituents or dependencies).

this work has benefited from the French State support via its "Investissements d'avenir" programs ORtOLANG (ANR-11-EQPX-0032), BLRI (ANR-11-LABX-0036) and A*MIDEX (ANR-11-IDEX-0001-02).


Requirement : two components (A,B) have a requirement relation when the presence of one requires the presence of the other.

it is possible to compare some specific properties, in accordance with established practices in typology.


These tools support a wide range of annotation tasks and are capable of being used for dependency annotation.

the customizable options accessible from the settings panel are as follows:  to save the state of the tree annotation, the user clicks the Save tree button.


Our hypergraph can be represented as a n × m incidence matrix with entries is the number of times v i ∈ e j occurs in the corpus.

as in any tree structure, each leaf node has a defined path from the root node to itself.


More specifically it was designed to meet the following challenges:  In a nutshell the lemon model consists of a list of Lexical Entries representing the words.

to represent these it would be therefore necessary to define LVF specific semantic roles, which could be related for example with the semantic roles present in VerbNet.


JAPE (Binsted and Ritchie, 1997;Ritchie, 2003) is a computer program which generates simple punning riddles using templates with slots where words or phrases are inserted.

it is necessary to develop some mechanisms to select only the modifiers related to the sought meaning of the target concept, and consider in a special way attributes with imprecise values.


We focused on VerbNet (VN), a high-quality and broad-coverage online verb lexicon for English (Schuler, 2014), (Loper et al., 2007).

during the development of MMO it was not a goal to achieve good recall on the English side of the verbs.


In the Semantic Web, several different URI references can refer to the same entity and the ability to identify equivalent entities is crucial for Linked Data.

we consider our method applicable to any type of resources.


The life cycle typically ends with the distribution and publication of a language resource; this includes making a resource available by download or, for example, as Linked Open Data through an API.

section 5. concludes the article with an outlook on future work.


In cookpad, meals are classified into categories (e.g., Japanese style, Western style) and some meals are voted for by users.

a novel resource is required to promote food research.


Toolkits like Stanford CoreNLP (Manning et al., 2014) and NTLK (Bird and Klein, 2009) provide all-in-one solution for the most common NLP tasks, such as tokenization, lemmatisation, part-of-speech tagging, named entity extraction, chunking, parsing and sentiment analysis.

we have included experimental support for word2vec models (Mikolov et al., 2013), trained on the Estonian Reference Corpus 3 .


This will include training of researchers and students on methodological issues related to the use of digital tools, large data sets, the interpretation of results and the representation thereof in research projects.

the Department of African Languages at the University of Pretoria (UP): Over at least the last two decades this department has been highly involved in different kinds of data collection particularly related to lexicographic and terminological developments in the African languages.


this dependency is less severe than the dependency on Fedora Commons, i.e., it is possible to use another user interface without losing the main facilities of FLAT, e.g., support for CMDI and Handles.


Here, we store the values of the CMDI element /Funder/.

we highlight the mapping for the main data descriptors; for the following discussion, please consult Fig.


In our workflows, resources are not described by librarians but by the resource creators (who know their resources best).

to many other standards, CMDI is a grassroots movement.


The rule-based component includes rules that make use of syntactic properties of Czech (and operate on word chunks, phrases, or whole sentences), rules that identify and disambiguate phrasemes, and heuristic rules.

the automatic syntactic annotation (parsing) was performed by turboParser (Martins et al., 2013) trained on the PDt data.


The underlying logic as well as elements of the graphic interface are presented below.

exceptionally, a software license may cover documentation and data that cannot be separated from the software itself (like in an XML file), but whenever the separation is possible, it is strongly recommend to respect the dichotomy between data and software licenses.


It can perform the following text analysis tasks: 1) named entity recognition (main component NER recognizer, F-80%), identifying such entities as personal names, company names, geographical names, expressions of time, quantities, monetary values, percentages, etc.

since there was no preceding experience in building such an NLP framework for Lithuanian, the exploitation of non-distributed LKSSAIS in real-life conditions has revealed that quality of service can be provided only on up to 3 billion word corpora.


As far as CODE ALLTAG is concerned, despite its undisputable relevance, we leave this topic for future research.

for the English language, e.g., an American (ANC) (Ide, 2009) and a British (BNC) (Aston and Burnard, 1997) variant has been assembled, for the German language, currently two synchronous resources exist, DEREKO (Kupietz et al., 2010) and DWDS CORE (Geyken, 2007); as a diachronic extension going back until 950 AD, the DTA (Deutsches Text-Archiv) (Geyken and Gloning, 2015) is currently going to be set up as an historic complement.


When subjects submitted their e-mail donation to CODE ALLTAG S+d , as a follow-up, a questionnaire was immediately sent to each donor asking for eight demographic variables and self-assessments of the donor, including gender, age, regional provenance, educational and professional background, language provenance and preferences, proficiency of writing and frequency of e-mailing.

writers of e-mails may face formal communication requirements much more often than authors of blogs, chats or tweets.


We also list, for each surface morpheme, what the "output" properties are for that morpheme.

the morphological specification used by the annotation tool is purely concatenative, represented in a human-readable text file with a list of possible combinations of affixes that can precede or follow a stem, in effect compiling out the different orthographic changes that can occur in tokens as a result of certain types of affixation.


Also, there were open sources of Turkish vocabulary available on the web in an accessible electronic form, but that was not the case for Hausa.

the abstract morpheme (I)m/POSS_1S has a slightly different twist, in which the I appears or not (and in different realizations) depending on whether the previous morpheme ends in a consonant.


The first was by calculating the naïve coverage and mean ambiguity on freely available corpora.

there are a number of alternations that are purely due to orthographic convention (such as ‹я› standing in for what would otherwise be ‹йа›) and complications due to the presence of many Russian borrowings, which are quite frequently left in their original orthography.


We went through this list word by word and added each word we knew to the lexicon along with the corresponding paradigm that it belonged to.

there are a lot of websites, books, blogs, etc.


A morphological analysis of a word involves describing one or more of its properties such as: gender, number, person, case, lexical category, etc.

it is also spoken by people in various other countries.


Both nouns and verbs can receive much of the same morphology with different meanings being intended by each.

we begin by introducing the Coptic language and giving a brief outline of its word structure.


The sibling pson tsOne The child pzEre tzeere The old person phLlo thLlO The dog puhor tuhOre Table 2: Alternation of Masculine and Feminine forms in Sahidic Coptic (Lambdin 1988:1) The same effects can be seen within the Egyptian number system which, like the gender system, does not usually mark directly on the noun.

morphological irregularity within the classes of nouns is much more difficult to deal with.


For example, only one lexeme is considered since both its meanings ('vapour' and 'pair') have exactly the same inflectional forms.

the forms matching query are highlighted in the resulting inflectional table.


The MRC Psycholinguistic Database 1 contains roughly eight thousand abstractness ratings.

a notable exception is the work by Turney et al.


(2011), who used a method that overcome these limitations by applying an algorithm from Turney and Littman (2003).

we first collected several affective ratings for German.


These are the most frequent functors assigned (as frame attributes) to the third frame slot of valency-3 frame entries (the first two being ACT and PAT).

a steady confrontation with the evidence provided by more and more texts is needed, in order both to enlarge the coverage provided by the corpus-driven approach and to evaluate the quality of the contents of LV built in intuition-based fashion.


are not handled yet, nor are determiners or negation.

defini- 3 The 20 words in our sample, selected randomly using GNU shuf were the following: aircraft, characteristic, clothesline, contrived, cypress, dandy, efface, frustrate, incandescent, khaki, kohl, lizard, nightie, preceding, residency, rock-solid, scant, transference, whatsit, Zen tions in the 4lang dictionary can be stated using no more than 129 primitives.


is considered a wrong schema, while the correct construction is harmonizować z czymś, without the reflexive mark SIĘ, see fig.

each duplicate is connected with the frame in its own, independent way.


Observe that a non-lexicalised phrase type xp(mod) (from another schema) is connected with it as well.

for instance, an Instrument for PISAĆ 'write' could be a pen, a ballpen, a pencil etc.


In particular, this concerns reflexive and non-reflexive verbs, provided that they represent the same meaning (diathesis alternations, e.g., ZBIĆ and ZBIĆ SIĘ 'break').

we directly link semantic arguments with corresponding syntactic positions.


there- Figure 11: A screenshot with a semantic frame and schemata being its syntactic realisation fore, na umór is treated as syntactic realisation of Manner , cf.


Each construction is associated with an example and the (possible) idiosyncratic verbs that block that specific pattern.

we provide the following definition of verb class: verb classes are sets distinct senses of verbs that share common argument realization patterns (i.e., they share the same constructions) and that profile the same template of event (i.e., they evoke the same conceptual frame).


The main difficulty with the anatomical terms and words is that they convey very specific and precise meaning, are seldom used by non-experts in medicine and keep close links and resemblance with the corresponding Latin or Greek words; • chemicals (e.g.

the annotators do not have medical training nor they present specific medical problems.


Prior research (Bär et al., 2012) suggest that this limitation can be addressed using word relatedness measures.

this line of research is focused on interlinking manually created lexical resources.


At the same time, corpus-driven approaches are strong at adaptivity -they can be re-trained on a new corpus, thus naturally adapting to the domain at hand.

the corpus-based model cannot learn a fine-grained polysemic inventory featuring tens of senses.


In (Agirre et al., 2006), automatically induced senses are mapped to WordNet via hand-labelled instances in the training set.

one can interpret a sense vector by comparing it to other vectors.


Hence, we have the semantic roles of AGENT and PA-TIENT.

• PIVOT: OBJECT that appears together with THEME, but that has higher semantic stress than the latter in the clause.


The aim of this project is to link together different predicate resources via manual mappings.

with this purpose, the event detection system is a pipeline which contains a set of modules to perform various NLP tasks.


We described the acquisition of VERBCROCEAN, a broadcoverage repository of semantic relations between Croatian verbs.

as most English patterns do not translate well into Croatian, we decided to extend our list of patterns.


These synsets were then aligned with the synsets already present in PULO, allowing the addition of new variants of the sense 2 .

this second evaluation is based on the same principles as the previous one.


Other than this future work on this specific enlargement experiment, we are working on algorithms to validate variants and synsets already existing in PULO.

both these approaches will be explained in the next two subsections.


The problem for NBSVM is that in the SerbMR-3C dataset the sentiment classes are not clearly separated, since the neutral class does not contain some objective information but rather a mixture of positive and negative sentiments.

pluses / minuses in the 1-10 scoring systems were ignored and X.5 scores were rounded down to X.


We have also been able to combine our findings with the NBSVM classifier for an even better performance on binary classification.

the contents of different sentiment classes in their news domain datasets were drawn from sources which systematically differ from each other not only with regard to their sentiment but also their topic, leading to high topic bias (Brooke & Hirst, 2011;Ferschke, Gurevych, & Rittberger, 2013).


Among the other features, WV is still the most informative feature.

there are several general concept nodes connected to the root node, each of which connects to several more specific concept nodes as their children nodes.


3 https://code.google.com/p/word2vec We performed three sentiment analysis tasks: opinion extraction, polarity classification and the combined task.

we are lacking this kind of resources for the Chinese language.


The sentiment labels of each word in ANTUSD were collected from two datasets: NTCIR MOAT Task Dataset and Chinese Opinion Treebank.

as these sentiment corpora were for exploring different research problems, their annotation scheme were also different.


In this example, the main verb is أحب [like] and the negation particle depends on the tense of the verb ل for the present, لم for the past and لن for the future.

actually, it is true that there is a common language that is understood by the majority of educated arab people because it is taught at schools and it is the main language of the media in most arab countries.


This paper deals with building linguistic resources for Gulf Arabic, one of the Arabic variations, for sentiment analysis task using machine learning.

we started with building linguistic resources for this dialect, i.e.


The class indicates the part of speech (POS) tag and the polarity of that word.

here, P and N denote the sets of positive and negative sentences of the corpus, respectively.


Then, our goal is to design a conversational strategy that makes it possible to maintain some control over the selected response of the system.

92% of the utterances of the system are unique in a dialogue on average.


We use the Weka Machine Learning Library's naive bayes classifier in our experiments (Hall et al., 2009).

that corpus contained a much lower percentage of guessable clues than did the human generated clues in the corpus collected by (Paetzel et al., 2014).


(2014) leveraged the existence of bilingual subtitles as a source of parallel data for the Chinese-English language pair to improve the MT systems in the movie domain.

to calculate the term weights according to the appearance of a term within the document collection, we apply term frequency-inverse document frequency (tF-IDF) (Ramos, 2003) as one term-weighting model.


A number of successful approaches, therefore, combine different types information.

using contextual information with the aforementioned enhancements helps to distinguish Machine Translations (MTs) that are different from the human translation and still essentially correct from those that share a high number of words with the reference but alter the meaning of the sentence due to translation errors.


In addition to the three MT results, we prepared two kinds of human translations.

if it is used as an automatic interpreter on smartphones, it is a totally different type of matter.


Additionally, this study improves on previous works by using actor model, which enables us to take advantage of all available cores by highly parallelizing the corpus building process.

it is therefore worth investigating how early deduplication, e.g.


This can either be the consequence of a linguistic ambiguity or of a tagger error.

• The rarest word in the sentence should have a rank of about 32000.


We declare an element Y of an accepted frame X optional if X \ Y is also an accepted frame.

perhaps the most surprising result evident from this table is that the short vector and the long vector conditions are quite competitive: this lends support to a recent finding of (Levy et al.


We address the task of automatically correcting preposition errors in learners' Dutch by modelling preposition usage in native language.

as we can see from the tables, results show that the system is very accurate for some prepositions (van, in, te, op), while not so much for others (als, bij, door).


The best performance on DUC 2004 is reached by the composite kernel CK according to both ROUGE-1 and ROUGE-2 scores.


In other 13% of the cases, both the verb and the particle are translated independently of each other, yielding an incorrect literal translation such as tourner sur for 'turn on'.

for English, we created a small corpus of verb-particle split constructions from scratch.


In the automatic alignments of parallel corpora, most of the particles align to NULL.

approximately 4.5 million sentences were used for training, combining Europarlv7, CommonCrawl and News data.


NULL alignments such as those illustrated in (7) are potentially detrimental for MT quality since they add weight to incorrect translations.

table 5: Summary of English-to-French word-level alignment of verb-particle split constructions.


In the example cases, if the clue (here: the quantification) is present, the respective noun groups (e.g.

for "bad requirement" the true positive value is 94, there are 2 false positives and 0 false negatives.


In this paper we present recent works contributing to transformation of the initial PolNet, a Polish wordnet developed at the Adam Mickiewicz University, into a Lexicon Grammar of Polish.

in all such cases we keep the corresponding synsets related by the transformational relations which describe the differences among their morpho-syntactic properties.


In (applied) linguistics and (second) language acquisition, we find more awareness of coding schemes and reproducibility of studies.

there are no agreed-upon coding schemes.


They display results of the searches as concordances.

as for the 'focus' words (the new information in the sentence), the frequency distribution of their functor attribute was a bit different.


This work has been partially funded by the SpeDial project supported by the EU FP7 with grant num.

two evaluation metrics were used: two-class (binary) classification accuracy and Pearson correlation coefficient with respect to human ratings for the Greek and the English language.


As for Hungarian databases, to the best of our knowledge, only one Hungarian sentiment corpus has been made so far, the OpinHuBank corpus (Miháltz, 2013).

moreover, it is also important to point out that negations, irreals and decreasers (Intensifierminus) are more frequent in the negative fragments than in the positive ones; e.g.


We develop a systematic account of the inventory of possible functor types and their interrelationships in Section 5.

for instance, the functor embedding a state of possession in Table 1 is to be read as follows.


We construct the utterances for the survey items by varying the stimuli along the dimensions shown in Table 11.

the remainder of this paper is structured as follows.


3 shows an example of an extraction rule derived from the sentence Die EU profitiert davon, dass sie andere Länder zwingt, auf Wachstum zu verzichten.

neither could we expect to have a complete set of extraction rules, given that we started with 120 sentences forming their basis.


For instance in The minister does not force the president to cheat nothing happens at all, neither minister nor president receive an effect.


Future work includes the expansion of the lexicon, and experimenting with other types of datasets.

the combined lexicon offers a slight improvement in the Egyptian dialect when carrying out 10 FCV, but decreases the accuracy in the Saudi dialect dataset for the same experiment.


As expected, the introduction of the neutral class causes the accuracy drops dramatically for both datasets.

manual additions and re-validations have been conducted over the past two years.


In the case of hybrid dependency patterns+CRF method, CRF part was evaluated in a 10-fold cross-validation scenario on the review dataset.

• For the method based on shallow parser, a JSON or SOAP format defined according to multiservice specification (Ogrodniczuk and Lenart, 2012).


This treebank is a result of parsing 20000 Polish sentences with the syntactic parserŚwigra.

any method of sentiment identification can be used with the tool.


These results underline the difficulty of this first step, extracting the correct aspect terms is a challenging task and it would definitely benefit from additional optimization experiments.

data statistics for both corpora are presented in Table 2 and in the next sections we will have a closer look at the actual annotations.


In building the SRL model, the following features are adopted: Features related to the current node: the syntactic node label, the extracted functional word, the extracted syntactic labels which are extracted from the subtree node during flattening, the linguistic features (such as voice, tense, aspect, etc.


Currently, FrameNet and PropBank are the two most commonly used semantic resources in Semantic Role Labeling (SRL) and SMT tasks.

as an example, the bilingual tree pairs in Figure 3 and Figure 4 have the same semantic meaning.


Besides, it is also found that the work of those early stages (e.g., syntactic tree and word alignment annotation) notably influences our work.

the steps (4) to (6) will be repeated iteratively until all sentence-pairs are annotated with their case labels.


With this iterative process, we incrementally improve our SRL model and save annotator's effort via providing more accurate pre-labeled case trees.


In analytic languages, such as English, one word usually equals one morpheme.

morpheme alignment annotation is manually performed by LDC annotators.


We first align articles on the same topic in Wikipedia via the interlanguage links ((1) in Figure 1).

the second line of table 4 shows the percentage of infinite scores of "+NN-ASPEC" for the character and word based NN features, respectively.


Intuitively, because the NN features give a measure of the bilingual similarity of a sentence pair, they could be helpful for this task.

as comparable corpora are far more available, many studies have been conducted to extract parallel sentences from them for MT.


It is most surprising in the case of Galician, because being co-official with Spanish it was expected that there would be at least regional government related multilingual accounts.

the boxplot shows a similar error distribution between ca-es and eu-es language pairs, and in fact, the average error rate per account was 21% for eu-es and 18% for ca-es.


The error rate in C dev is 3% (ca-es) and 33% (eu-es).

it does result in high quality parallel corpora when such Twitter accounts are available, which can be exploited to adapt an MT system to the new domain presented by Twitter.


Researchers make available new findings and knowledge in biology and medicine through the publication of research articles.

regarding percentages of titles and documents in the training data, table 1 shows that more abstracts are available in comparison to titles.


ACL WMT'16) so the research community can experiment with additional translation methods.

only titles are available in more than one language in PubMed (Wu et al., 2011).


In addition to this, it is worth noting that many websites are available in several languages and this translated content is another useful source of parallel data.

to deal with this limitation, we propose a new method that focuses on crawling top-level domains (tLD) for multilingual data, and then detects parallel data inside the crawled data.


Our analysis indicated that while there were 3 comments made about accuracy out of 744 people with correct predictions (0.40% commented), and 13 noting incorrect answers out of 201 with incorrect predictions (6.5% commented).

it is unknown whether the up-and down-voting was motivated by the accuracy of the quiz, but researchers making interactive prediction sites may discover that inaccuracy is in fact more engaging in some regards.


The word embeddings are again the best of the individual feature sets in terms of recall, F1 score and accuracy.

wikipedia A dump of English wikipedia from 1 September 2015.


A popular approach used by recommendation systems is collaborative filtering for identifying users similar to a target user based on their purchase history, and then recommending products that similar users have already bought but the target user has not (Schafer et al., 1999;Sarwar et al., 2000).

figure 1 shows an example of a portion of the tweet timeline of a Twitter user who wants an iPhone.


To determine whether monthly aggregation is changing the rate at which prevalence estimates converage, for each sampling level we compute how close an estimates' correlation with GFT is to the correlation's maximum value.

they also find that twitter's 1% sample is similar to the Firehose, however, indicating that most of the bias they observed is introduced by the Streaming API.


for 10-CV evaluation), training/testing splits were defined with respect to the overall and per class proportions of tweets indicated in Table 4 (%All column).

we defined four communication categories to classify tweets in our work: 1.


In the former, words occurrences are assigned sense labels from a predefined sense inventory.

we then asked the annotators to cluster these words into sense clusters, giving them as much freedom as possible, and allowing for one word to appear in multiple clusters (soft clustering).


Both resources are large, manually constructed networks intended to describe the language in general rather than any specific domain.

each entry except a special root is connected to other entries, its semantic descriptors.


From a machine translation (MT) perspective, the deeper the processing of utterances, the less language-specific differences will remain between the representations of the meaning of the source and target texts.

afterwards, improvements over the baseline system have been applied, adapting and replacing some of the original sieves (Soraluze et al., 2015), taking into account that morphosyntactic features are crucial in the design of the sieves for agglutinative languages like Basque.


In case of ambiguity, the algorithm picks up the most "popular" article.

this paper presents the first release of such corpora, which includes NERC, NED, WSD and coreferencelevel annotation for these six languages.


The changes we propose for the original VerbNet role inventory in Figure 1 are kept as small as possible.


Hindi vocabulary has a number of foreign language words, mostly taken from English and these find a place in Hindi WordNet as well.

f-score of WfS baseline on Health, Tourism and News domains was found to be 60%, 65% and 55% respectively.


It was seen that most of the time the literal senses of a word were placed in ranks above the metaphorical or figurative uses.

hindi WordNet 1 (hWN) is developed for capturing the fine grained senses of hindi language.


These results are as a rule not sorted by their relation to different ambiguous query senses and returned in the order of their relevance ranking, which for many of them seems to be almost equal.

in Section 2. we introduce our approach and put it into the context of the previous work in the field.


this type of multi-modal data offers an interesting basis for vision and language research but most existing datasets use crowdsourced text, which removes the images from their original context.


A more comprehensive analysis of the cases where the system and the human disagreed on classification is reported in the conference presentation.

in this paper we present a multiple instance learning-based segmentation system that accurately labels 91.27% of the video frames of 500 continuous utterances (including 7 different subjects) from the publicly accessible NCSLGR corpus <http://secrets.rutgers.edu/dai/queryPages/> (Neidle and Vogler, 2012).


(It involves a kind of acting out of rolling dice; the linguistic properties are not typical of lexical signs.)

the total feature vector length is 14; and each frame would have approximately from 1 to 6 feature vectors corresponding to the high local motion regions.


In step 2, we extract features related to the shape and velocity of the moving regions (e.g., hands).

there is no fixed fingerspelled vocabulary: many fingerspelled productions would not be included in any ASL dictionary.


The best performing algorithm performed global optimization over the input sentence set.

a supervised approach with lightweight and efficient features improves over the lead-based baseline.


The second kind, neoclassical compounding, combines some elements of Greek or Latin etymological origin, such as hydro + logy = hydrology.

some prefixes are very close to the neoclassical roots, compare prefix biwith neoclassical root uniaccording to (Béchade, 1992).


The UniMorph Schema includes the features necessary to distinguish all these categories, which are marked by surface contrasts in each language and are not decomposed further in any natural language.

we present methods inspired by linguistic fieldwork for gathering inflectional paradigm data in a machine-readable, interoperable format from remotely-located speakers of any language.


In other word, user can mentally create his own virtual app on top of existing ones.

there was no effort to conceal the wizard arrangement from the participant.


The wizard was instructed to respond directly to participant's goal-directed requests and to not accept out-of-domain inputs.

sometimes users produce utterances which may involve several apps, e.g., "Boost my phone so I can play [game] spiderman" requires CLEANMASTER to clear the RAM and the game SPIDERMAN.


8 The inter-annotator agreement before reconciliation was 0.89 and after reconciliation became 0.95.

3 http://ii.tudelft.nl/genius/ 4 https://github.com/vaskonov/tutorial We used a frame-based semantic representation, where one or more composite semantic labels are used to represent each utterance in a dialogue.


Hu, Walker, Neff, & Fox Tree, 2015;Tolins, Liu, Wang, Fox Tree, Neff, & Walker, 2013, Hu, Dick, Chang, Bowden, Neff, Fox Tree, & Walker, 2016.

participants in lab-based behavioral experiments often experience a task that is already somewhat routine.


entities expressing frequency or quantity).

term variants in the UMLS do not always instantiate this pattern: e.g., concept C0003611 is designated by the term appendicectomie, but not opération de l'appendice.


1 Moreover, both of these corpora have additional shortcomings.

we would like to acknowledge Kellen Maicher who created the virtual environment and Bruce wilcox who authored ChatScript and customized the software for this project.


On the one hand, given the objective of the study, a fine grained study of the form -function relationship requires a fine grained functional analysis, too.

the campaign was realised on a duration of 2 months for most raters.


Feedback utterances are among the most frequent in dialogue.

with the suggested annotation schema, a good number of annotators and high quality recordings, it is possible to achieve good inter-annotator agreement on large collections of instances.


In this paper we discuss the design of the application and the influence of metadata and various forms of feedback.

but this was the closest we could get to a simple score.


Judgements of pronunciation quality were obtained from experts (Burgos et al., 2013;Burgos et al., 2014).

almost three times as many women participated and they transcribed 1.5 times more items on average than men, which is a significant difference (t' (130.328) = 2.203, p =.029).


National tests have been collected since 1996.

the main purpose of the corpus is to facilitate the systematic and quantitative empirical study of the writings of various student groups based on gender, geographic area, age, grade awarded or a combination of these, synchronically or diachronically.


Students are aged between 18 and 40 years (80% aged 18-30) and represent 14 different mother tongues (we provide in brackets the number of texts per first language): Chinese (323), English (142), Spanish (139), German (76), Russian (70), French (43), Japanese (50), Italian (34), Dutch (15), Tetum (22), Arabic (13), Polish (22), Korean (9) and Romanian (8).

we established a set of 7 fields that were required for the productions of the informant to be included in COPLE2: name, age, nationality, gender, mother tongue, knowledge of other second or foreign languages, period of time studying Portuguese.


We chose the realization of the velar nasal consonant /N/.

the women were aged between 22 and 47 years, the men between 30 and 45.


GG speakers are used to read in German some of them privileged a fast reading in order to finish quickly others chose to read aloud carefully in order to interpret the stories.

this result could be explained by the written input.


Precision is calculated on the level of a set of candidates where each set containing the right word is considered correct and the set not containing the correct word as incorrect.

text samples produced in a digital form can be identified in the corpus as they are marked as such and can be analyzed separately.


In case of both presented technologies, the optimal number of candidates turns out to be 3 to 4.

every text sample was annotated by only one annotator and no inter-annotator agreement was measured.


These ways and techniques are usually mastered by linguists through their experiences in a field and from a limited amount of knowledge gathered from books or lectures.

in our three projects we have confirmed that using a common application among linguists is an unrealistic solution and using a shared scheme such a standard proposed as multi-link-path model, is also unrealistic or at least unsuitable for linguists.


A training programme must take this variability into account, in order to deliver appropriate measures for the different conditions and needs of languages with respect to their digital usability.

finally, DLDP will deliver a number of recommendations specifically addressed at language stakeholders and policy makers, the Roadmap for Digital Language Diversity.


The digital readiness of a language is inextricably linked to its digital presence: whenever a language is technologically supported and thus widely digitally usable, its digital representation expands.

provision of state-of-the-art language-based applications, which would enable and foster their use over digital media and devices, is severely limited (Mariani, 2015).


The darkness of the color in the map reflects the relative frequency of the selected variant in each location.

for example, distribution maps showing the distribution in the Tuscan dialect area of a specific dialectal form (selected via the ALT-Web website) are easily obtainable.


We also use crowdsourcing to obtain a portion of the translation for some languages, although not all languages have sufficient numbers of crowd workers with the required skills to perform translation.

the first set of LORELEI language packs is expected to appear in LDC's catalog in late 2016.


According to the Ethnologue (Lewis et al., 2015), the three dialects to the South of the Danube were developed between the 5th and the 10th century, while according to Rosetti (1966), this process took place after the 10th century.

we report the number of correctly classified and misclassified instances.


The higher the possibility of the translation pair candidate being selected correctly is (determined by the structure of the transgraph), the lower is the cost to be paid of adding any new edge to it.

an arrow line denotes derivation of word from proto-word and a dashed edge denotes high possibility of shared meanings between two words, and thus denotes the possibility of missed meanings in the transgraph.


Consequently, when w A 2 also shares the same meaning with pivot w B 1 , there is a high possibility that proto-words w P 1 and w P 2 also share the same meaning.

the rest of this paper is organized as follows: In Section 2, we will briefly discuss closely related languages and methods in comparative linguistics.


The new translation pair candidate induced from two new edges like candidate 8 (w A 3 -w B 2 -w C 1 ) in Figure 4 is too strong to be considered.

there is a high possibility that w C 2 also share the same meaning with pivot w B 1 .


The geolocations were further manipulated: Multi-part expressions were split as described for the concept list in 3.1 and additionally extended by plural and genitive endings.

for smaller places, we manually checked whether the place reference could contribute to re-identification of any third person mentioned.


However, some experiments showed that completely blackening that timespan invalidates the whole sentence for further linguistic analysis as suprasegmental signals are disturbed.

it is obvious that any method working on the translation of the language data instead of on the original data will be skewed.


Statistics for the crowdsourced dataset are presented in Table 1.

in this section, we present the methodology of crowdsourcing the entity salience corpus as well as its specific features.


In this paper we present a gold standard dataset for Entity Linking (EL) in the Music Domain.

the ELVIS code is available at https://github.com/sergiooramas/elvis.


tagging 'Yellow Submarine' as dbpedia.org/page/Yellow Submarine (song).

this information is extracted from the whole LASt.FM corpus for those entities falling in one of the four musical categories previously defined.


Our method relies on the argumentum ad populum intuition, i.e.

we decided to select for the final ELMD dataset only those URIs stemming from a typeequivalent setting where agreement score is equal or greater to 1.


Instead of expressing [ɛ] with ARABIC LETTER AE, they create the appropriate on-screen letter form by typing ARABIC LET-TER HEH and then, when necessary, preventing it from joining with the following letter using the special invisible character ZERO WIDTH NON-JOINER U+200C.

an entirely digital complication in processing Sorani text regards the Unicode expression of the very frequent short vowel /ɛ/ (also transcribed as /ae/), which is expressed by a special variant of the arabic letter heh .


If only lung (ANATOMY) or cancer (PROBLEM) were recognized, the evaluation would yield only 0.5 true positives.

the type of relation that can hold between the top-level entity and the nested entity is defined broadly and includes specialization and attribute, among other types of semantic modification.


Where the German DBpedia links were not available, the owl:sameAs links were taken from the English DBpedia entities, as such links were available for most of the entities from the dataset.

since NEL or KBP evaluation tasks might require a new corpus or at least a new gold standard, and the creation of such resources requires significant effort, there is a desire to automate steps in the corpus creation process.


During the initial stages of the project, the main focus was the development of the annotated data sets for each of the languages.

most location and organisation named entities in the conjunctive languages require a nominal prefix that is followed by a capitalised character, e.g.


In the light of the above, it appears that NE resources are quite well developed, benefiting from two decades of work.

knowledge base population, anonymisation and information extraction at large) (Fort et al., 2009).


This strategy involves the misclassification of some mentions of the same entity in a single document.

due to the document length (≈ 380k tokens), some mentions belonging to person entities, with many occurrences in different contexts, were frequently labeled as locations and organizations (and even as miscellaneous entities, not shown in the table).


A mention is selected for analysis if (i) it is not the first mention of the text, and (ii) it is the first mention of the entity it belongs to.

the heuristics also select the best named entity class for each entity, thus correcting previous NER errors.


3M sentences matched by seed facts were utilized to learn more than 1.5M pattern candidates for the relation extraction task.

when annotating ontological relations, we used the wordNet ontology (Fellbaum, 1998) as our reference.


We can say, then, that the bar exam encapsulates a range of legal knowledge.

mel's mistake negated the required specific intent.</t> <h>mel should be acquitted.</h> </pair> A range of other structural issues of the text were identified and controlled for in order to produce a corpus that conceptually matches the original: • meta comments about the exam question, e.g.


From RTE 1-3 (Dagan et al., 2006;Bar-Haim et al., 2006;Giampiccolo et al., 2007), it was a binary classification problem for only two relations: YES and NO, regarding to entailment and non-entailment.

• (Doug Lawrence bought the impressionist oil landscape by J. Ottis Adams in the mid-1970s at a Fort Wayne antiques dealer.)


The Cohen's kappa coefficient of interagreement on RTE+SR was above 0.7 in overall.

the similarity may become even a fuzzier concept.


Alternative configurations of the model are described and compared to each other and to LDA, the most popular topic model.

comparing both configurations of the SemLDA, the topics obtained with the second experiment (All senses / Semcor) seem to be more suitable than those of the fourth experiment, where WSD was applied.


Wikipedia was used because it provides a large and wide-coverage source of text, completely independent from the datasets used and from WordNet.

for each word in the corpus that is in one or more WordNet synsets, if there is a previously trained model, probabilities P (w|s) are predicted from the distribution of LDA.


Since a test collection is a collection of patients, it represents a patient population.

relevance judgments for this topic required an average of 2 minutes per patient.


For the most part, the system performed well with an average f-score of 94.7 across all entity types.

figure 1 shows a sample x-ray report and Line 9-11 is marked as a rationale snippet for CPIS/PNA.


For annotation, we use a lightweight, web browser-based annotation tool called BRAT (Stenetorp et al., 2012).

we can treat the event detection task as a dependency parsing problem, as discussed in Section 4.2.


Preliminary experiments showed that all these factors made it difficult to replicate the Hindi-English experiments for this data.

shows the mapping between English phones from the phone set used in our standard US English build and Hindi phonemes from the Indic phone set, along with the phonetic features assigned to each phoneme in both sets.


(Sriram et al., 2004) describe a technique for multilingual query processing, in which words in the queries are converted into a language independent 'common ground' representation, after which a weighted phonetic distance measure is used to match and rank queries.

it seemed like our approach was viable and was not influencing the quality of the system negatively.


These include time and date information which Taidhgín can deliver on request, as the tags are set to the nearest hour in the time zone specified.

a further priority will be the development of speech recognition for Irish, which is also part of future research plans.


It was susceptible to concatenation errors if the waveform coverage in the voice database was incomplete but in that period much progress was made using as little as one hour of recorded speech and the samples in the corpus are all produced from such small databases.

the corpus is freely available as an Open Access resource (Gratis & Libre) for research use and as a historical archive under CC-BY (Attribution) licensing.


The timeline for the audio is then used to annotate the beginning timestamps for the gestures: as shown in Figures 9 and 10, in front of every gesture, a time is shown inside a pair of square brackets to indicate the beginning time of this gesture stroke.

a3: What alleviated the people of the riot?


The humangenerated dialogue corresponding to Figure 1 is shown in Figure 2.

in this annotation, a gesture stroke is positioned 0.2 seconds before the beginning of the gesture's following word.


this corpus could be useful for researchers investigating how personality affects dyadic interaction on verbal and bodily levels.


Participants were selected from a larger sample to be 0.8 of a standard deviation above or below the mean on the Big-Five Personality extraversion scale, to produce an Extravert-Extravert dyad, an Introvert-Introvert dyad, and an Extravert-Introvert dyad.

the three interactions for the current study were more abstract in nature, providing a novel conversational format for the analysis of nonverbal expressive alignment.


In total, we did two separate runs of the evaluation stage, one with Japanese instructions and one with English ones, to test again if the language of instructions has any effect on the received results.

there may be English seed verbalizations with more than one possible meaning, or which could be used to verbalize more than one ontology element.


Accordingly, workers were only shown sentences of the form X was constructed in [year] Y for this ontology element, and we only retrieved Japanese verbalizations at around the same level of semantic specificity.

we ask Japanese crowdsourcing workers to provide Japanese translations of the English verbalizations, with each Japanese translation being understood as a potential verbalization of the ontology element linked to the original English verbalization.


For both resources, the FLYING IN AN AIRPLANE scenario is most diverse (as was also indicated above by the mean word type overlap).

as can be seen in Table 2, there is some variation in stories across scenarios: The FLYING IN aN aIRPLaNE scenario, for example, is most complex in terms of the number of sentences, tokens and word types that are used.


They annotated event-denoting verbs in the stories with the event labels and participant-denoting NPs with the participant labels.

extraction of script knowledge from large text corpora (as done by Chambers and Jurafsky (2009)) is difficult and the outcome can be noisy.


Syntactic information may help in the identification of the correct temporal value but deciding among fine-grained temporal relations is not easy.

for temporal expressions we can observe a more conservative approach (e.g.


We then start comparing the span size (by means of the tokens' offset) and content in order to promote and select tokens and multi-tokens annotations.

machine performance with target entities given obtains good results (F1 0.564 for English and F1 0.736 for Italian) suggesting that detailed annotation guidelines can contribute to the performance of automatic tools but are often difficult to follow.


In this paper we will present the details about the creation of the Boulder Lies and Truth Corpus (BLT-C) along with some preliminary results based on a study leveraging such a corpus.

lying Words: (Newman et al., 2003) created a labeled corpus of elicited narratives marked as lie or true, then applied machine learning techniques (logistic regression) to rank the contribution of these linguistic categories.


• There are more than three edit operations in a row.

yet another result is the identification of possible errors (spelling mistakes, encoding problems, etc.)


Overall, STACC provides the best balance between the number of alignments it can identify and the precision in identifying correct alignments.

in LEXACC, a core part of the final score is computed by evaluating each source token against each target token and measuring translation correspondences using lexical translation probabilities for each token pair.


As we saw in Section 2.2., aligning complex lexical units requires creating one-to-many/manyto-one as well as many-to-many links, which represent a known difficulty for WA models.

here, the verbs "spaccando" and "ripping" are rare (both F[1,15]), while "stanno" and "are" are not rare.


In automatic machine translation evaluation, outputs of an MT system are compared to a reference translation, i.e.

we evaluate also using Meteor with no paraphrase support (MeteorNP).


As with the DM corpus (Oepen et al., 2014), DEEPFTB is comparable in the sense that the semantic arguments of verbs and adjectives are made explicit, but it leans a little less towards a semantic representation (hence the "deep syntactic" name).

an increasing number of works have been proposed over the last few years to cope with graphs (Sagae and Tsujii, 2008;Flanigan et al., 2014;Martins and almeida, 2014), whether acyclic or not.


Another argument against the use of prepositions for denoting semantic relations is that in our framework, lexicon, syntax and semantics are considered autonomous levels of representation/analysis of the language.

the tIC group is further classified into semantic relations.


KorAP already participates in CLARIN-FCS and further support is in preparation (e.g.

it supports very large corpora with multiple annotation layers, multiple query languages, and complex licensing scenarios.


Although it uses Lucene as well, it applies different indexing strategies.

in addition to the serialisation of user-formulated queries, the protocol supports query rewrites.


KorAP is developed at the Institute for the German Language (IDS), member of the Leibniz-Community 11 and supported by the KobRA 12 project, funded by the Federal Ministry of Education and Research (BMBF).

it can be a good candidate for performance comparison in the future and may serve as an alternative search backend.


Conversely, we observed that models trained on one type of corpus and applied on the other type always produce lower results.

we also evaluated the performances of the COLID models when the predictions made are used to infer the column separator.


For some words it was not possible to assign the grammatically correct POS tags due to compatibility issues with the disambiguator.

the BOUN sub-corpus was used as a representative of the adult language.


This is in agreement with previous research (e.g., Stadthagen-Gonzalez & Davis, 2006) indicating that highly imageable words tend to have lower AoA ratings, suggesting an early acquisition.

the CLC was created to represent children's language.


High imageability, then, points to low rated AoA values just as high frequency does.

the correlations of CLC>BOUN nouns with rated AoA were not significant.


Best results are obtained when taking into account both the probability of a form given its dediacritised version and the probability of the form in the given context (TM+LM).

overall, the best type of data for training diacritic restorers of both standard and non-standard texts is Web data.


As shown in Figure 3 we present the users with 3 variants of the same sentence: one produced by a template-based generator (referred as template in Table 5), one produced by the NLG statistical model (presented in Section 5.)

as for paraphrases, a variety of approaches have been tested including extracting paraphrases from multiple translations of the same source text (Barzilay and McKeown, 2001) (parallel corpus approach), using monolingual comparable corpora (Wang and Callison-Burch, 2011), aligning several dictionary definitions of the same term to extract paraphrases (Murata et al., 2005), etc.


For Portuguese, some such initiatives include Onto.PT 3 (Gonçalo Oliveira and Gomes, 2010), OpenWN-PT 4 (de Paiva et al., 2012), MultiWordnet of Portuguese 5 , Word-Net.PT 6 (Marrafa, 2002), WordNet.Br 7 (Dias-da-Silva et al., 2008).

as a second step, any relation that was not found in Onto.PT was manually validate by two native speaker human judges.


From the above table, relevance vector machine does dramatically reduce the model complexity at the expense of affordable prediction accuracy loss in comparison to SVM.

for comparison, we also train with support vector machine learning algorithm on all 165 features.


DCG's discounted factor relies on the position of each element, and this implies that the last four value of L list will produce different costs.

for this reason, it is often used in research (Lapata, 2006;Philbin et al., 2007;Järvelin and Kekäläinen, 2000).


It is important to note that in all these stopword lists, words are separate on white-spaces.

despite spoken by millions of people, Urdu is an under-resourced language in terms of available computational resources.


Both studies consider structural and statistical factors only.

it may be inferred that if the resources are built on properly-segmented words, we might have better results for PS summary corpus.


By identifying domain-relevant synsets and categories, we could limit the number of entities annotated from BabelNet to those specific to our domain.

a sample of 500 abstracts from the corpus is currently being manually annotated with these semantic relations.


Coverage with respect to the domain vocabulary shows the proportion of the concepts of a domain that are included in the ontology: it can be interpreted as a measure of recall.

section 2. describes how the texts of the corpus were selected and pre-processed.


We manually evaluated annotation precision on a sample containing 100 sentences, with 358 annotated entities and 932 annotations (an entity is thus linked to 2.6 resources on average).

other limitations stem from entity annotation errors, in particular bad delimitation.


The semantic analysis of scientific corpora allows to add new relation types and instances to existing ontologies (Petasis et al., 2011) or thesauri (Wang et al., 2013).

we experimented with combining domain-specific and generic resources to achieve a satisfying balance between annotation density and precision.


The training data is the merged TBAQ dataset.

the non-deverbal gazetteers contain 6528 and 745 entries for event and state hyponyms respectively.


The results obtained are then compared to the substitutions proposed by humans.

we propose in this paper to further explore this IR approach to build thesauri.


Contact and Transaction events are augmented with additional attributes.

for Rich ERE, there is more annotation on the English side than on the Chinese side at all levels, except at the entity level, in which Chinese has slightly more entities annotated than English.


An example of an annotation of this kind can be seen in Figure  4.

in the context of disorder, we decided to exclude a set of variants.


Indeed, conservatives sources frame Snowden as being a 'traitor' or 'disloyal' to his country, while liberals frame the story in terms of harm caused to the country.

differently from liberals who focus on both virtue and vice of their foundations, conservatives highly emphasize the vice aspect i.e.


This time frame consideration also allows accounting for negations, irrealis and sarcastic statements in text.

several factors need to be considered when one thinks of possessions and their attributes.


Our research is motivated by the affective-cognitive consistency model (Rosenberg, 1956;Rosenberg, 1968), a branch of cognitive consistency theory that not only hypothesizes that people are motivated to seek a coherent state both internally (at the level of thoughts, beliefs, feelings, and values) and externally (through attitudes and behaviors), but also that individuals gain more motivation in achieving a consistent state so that others perceive them to be consistent.

the value of the object is devoid of personalized information, thus allowing cross owner profile analysis (both A and B may possess a shoulder bag, but only A's bag is green).


temporal vs. atemporal) in confusion matrix as shown in Table 14.

3: Initialize previous stratified cross-validation accuracy to 0.


Table 11: Evaluation results of various n-gram models for the second step of two-step classification framework: gold standard test set experiments It is evident from the results obtained through different experimental setups that temporal classifier, in general, performs remarkably well while we deal only with two classes, namely temporal and atemporal.

distribution of seed words among the various temporal classes is important in order to ensure that temporal classifier is not biased to any particular class.


The key idea is to simplify the question as much as possible prior to input to the semantic parser.

in this work, our goal is to provide a sufficient number of question/logical form pairs to train a baseline semantic parser.


For example, one annotator marked just in just minutes apart as a MODIFIER(TYPE=APPROX).

formally: figure 22 shows an example BETWEEN annotation, along with a graphical depiction of its formal interpretation: the interval starting at the end of 1994 and ending at the document creation time.


), such as the past three summers, since this cannot be described with some prefix of a YYYY-MM-DDTHH:MM:SS date-time.

there are a few drawbacks of the ISO-TimeML approach.


As also indicated by the Conceptual Metaphor Theory (Lakoff and Johnson, 1980), the mapping across domains stands at the core of the metaphorical connection and this kind of connection is very commonly established in proverbs as linguistic metaphors.

based on the resulting annotations, we show how words belonging to different parts-of-speech and semantic domains contribute differently to the metaphoricity in the two languages.


We therefore describe the main traits of previous methodologies, before presenting ours.

roles are called "core" if they "instantiate a conceptually necessary component of a frame, while making the frame unique" (Ruppenhofer et al., 2006) 2 .


can be generated by calculating all combinations of then is x located at y a day before y verb ?


For geographical information, we are relying on the Openstreetmap (OSM) database 2 (Haklay and Weber, 2008).

the approach we want to adopt for reference resolution is the words-as-classifiers approach described in (Kennington and Schlangen, 2015).


So there exists two different kinds of labels.

for example in PerDT there is a metaverb PP SBJ OBJ "" to give which has a meta syntactic structure as shown below: ||subject, object, prepositional object (to)|| Ali book ACM 1 to Sara gave 'Ali gave the book to Maryam.'


The proposed method is evaluated for POS tagging tasks).

table 1 summarizes the tag set for entity annotation.


In the 1213 sentences in the ACM set, the numbers of identified entities and relations were 12463 and 11201, respectively.

the items labeled INTELLIGENT-AGENT characterize the ACM set, owing to several articles about electronic commerce.


It also indicates that the granularity of the scope of ATTRIBUTE relation is wider than that of others, i.e., properties can further be broken into several subtypes.

instead of precisely defining a frameset for in-domain events, we describe the roles of entities in the form of their mutual relations using a set of general relationships such as method-purpose, system-output, and evaluationresult.


(Gast et al., 2015b;Gast et al., 2015a).

since Reichenbach (1947) at the latest, it has been known that tense and aspect cannot be adequately analysed without taking into account a third component, labeled 'Reference Time' by Reichenbach (1947), and explicated by Klein (1994) as 'Topic Time'.


In the above sample, the frequency of '魚塘 (fishpond)' is 417 versus '乳糖 (lectose)' being 146 in the LM raw corpus.

our special thanks to all Kaldi community.


Text-to-speech has long been centered on the production of an intelligible message of good quality.

the distribution of the corpus across these three levels is as follows (with silences longer than 1 second excluded): Neutral (2955 sec.


With the emergence of standards for modern web technologies and, at least equally important, browsers that support them, it is possible to implement even complex software as web applications that run in a browser.

this means that certain parts, like uploading files to the server, displaying a preview, etc.


The non-standard word taxonomy was adopted from Sproat et al.

to WebMAUS this service does not require any orthographic or phonological transcript.


The current version of the SPA platform already integrates several modules and is available at https://www.l2f.inesc-id.pt/spa.

the parameters common to all the services, such as the input filename, are provided only once.


Identifying the dialog acts is important for spoken dialogue systems, since they reveal the intention of the speaker.

different ap-proaches have been tested, an improved MLP-based system similar to the baseline one including MLP retraining and low-energy frame dropping, but also other methods based on segment-level features in combination with neural network modeling and i-vector based classifiers.


That can be achieved by searching, for instance, the chain " Other annotations can be combined to achieve useful results for phonetic, morphological or lexical variation and change.

the use of a database allows simpler and faster search through different criteria.


One is the Penn Discourse TreeBank (PDTB) (Webber and Joshi, 1998), built directly on top of Penn TreeBank (Marcus et al., 1993), composed of extracts from the Wall Street Journal.

a final set of 16 discourse functions described in metaTED was achieved, and it is composed as follows: • ADD -collapsed from Adding to Topic and Marking Asides  The annotation of metaTED was done through crowdsourcing, on Amazon Mechanical Turk (AMT) 3 .


The second group (which, what and how) are at 79-82% percentage agreement between crowd and gold standard annotation, which is likely due to their more ambiguous answer surface realization possibilities, e.g., a what-question can ask for an activity ('What did Peter do?')

with respect to the observed differences between the annotation quality of the answers to different question form subtypes, our hypothesis is that since certain examples, such as answers to why-questions, exhibit a much greater variation in terms of their linguistic material, this leads to less consistent results in the annotation, especially for the crowd.


We compare focus annotation by trained annotators with a crowd-sourcing setup making use of untrained native speakers.

during this piloting process, the second author met with the annotators to discuss difficult cases and decide how the scheme would accommodate them.


Over the last decade, there has been active research in modeling stance.

the dataset has instances corresponding to six pre-chosen targets of interest: 'Atheism', 'Climate Change is a Real Concern', 'Feminist Movement', 'Hillary Clinton', 'Legalization of Abortion', and 'Donald trump'.


All of the data created as part of this project (the Stance Dataset, the domain corpus, the annotation questionnaire, etc.)

creating a dataset where 99% of the tweets are from this category makes the dataset less interesting and less useful.


It is clear that this method emphasized precision over recall, as it emulates a grep-style filtering program.

good night, Happy Birthday/New Year/Anniversary, Merry Christmas), in certain expressions (e.g.


In the "Relevance Analysis" task on T weet2014DS1, the crowd identified 476 out of 566 (88%) tweets as being relevant for "whaling", where the relevance score is higher than 0.2.

section 6. presents state-of-the-art approaches for relevance, novelty and sentiment analysis.


We extent this space with relevant tweets and news snippets and relevant event mentions in those.

for each tweet we derive an aggregated novelty score in comparison with the rest, by using a weighted schema: weight 1 if the tweet is more novel, weight 0.5 if the tweets are equally novel and weight −1 if the tweet if less novel.


used in similar context even if not together.

since tweets are very short texts, we expected that a small context window would work better.


The H words left and right of w i are extracted for every instance of w i in the corpus formulating a feature vector.

3) Every image is represented as a vector of visual words.


Let the semantic neighborhoods of a target word w i computed based on textual and visual features be represented as ordered sets (according to similarity) denoted as T i and V i , respectively.

for larger neighborhoods (> 50 neighbors) the fusion-based approaches perform con-sistently better than the baseline.


We might conclude from its low entropy that it would be worth checking whether the single annotated token of 'have been' is a rare case or an error.

it would be possible to increase consistency during the annotation process and not only on completely annotated corpora.


3 As mentioned in the introduction, since each word can trigger multiple event mentions having different types/subtypes, we train one CRF for each type.

in this section, we introduce our corpus and the event coreference task.


For example, if a third singular (3SG) person and number inflection is marked by an ascending tone in SJQ, here annotated as 42 (see table 1), the same 3SG will fit into the range of tone 32 in another Eastern Chatino dialect (as noted in section 2.3. below, table 1 shows the tone registers with 1 as the label for the relatively highest tone).

this approach may have a positive impact on linguistic science and language documentation in general.


This initial low-investment speech corpus allows us to train speech and language technologies for a more rapid extension of the volume of automatically annotated recordings, as well as further bootstrapping of speech and language technologies for the particular languages.

we record a high-quality speech corpus with minimized noise and optimized speech signal from prepared text to create an initial corpus with little time investment.


For the command recognition task, an equally-likely finite state grammar formed by all the unique possible command sentences was initially used.

nevertheless, the rather restricted read command recognition grammar used is favoured by the ground-truth segmentation that does not introduce insertions due to wrongly hy-  pothesized speech segments (and neither deletions due to lost speech segments).


The overall multi-room and multi-channel voice command recognition processing pipeline WER (%) performance results are shown in Tables 4 and 5 using ground-truth and automatic segmentation respectively.

in practice it was necessary to use an extended command grammar incorporating the background model to better handle inaccurate segmentations provided by the automatic SAD.


For the emotion dimension estimation, the automatic cross-corpus emotion labeling for the different corpus was effective for the dimensions of aroused-sleepy, dominant-submissive and interested-indifferent, showing only slight performance degradation against the result for the same corpus.

for all utterances, perceived emotional states of speakers are provided.


From the 91768 ST tokens and 104785 TT tokens in the T,P and D translation sessions under investigation only 13110 (14%) showed simultaneous gazing and typing activities.

the time needed to complete the translation of six texts was not restricted but usually took between 2 to 3 hours.


Neighborhood statistics were then calculated from the top 17,000 phonological words for all words and nonwords within each of the syllable segmentation schemas.

the final neighborhood statistic, neighborhood frequency (NF), is calculated by summing the subtitle word frequencies of all of a given phonological word's neighbors.


<tok form="obscuras" nform="oscuras"> ob-<lb/>scuras </tok> Figure 2: A TEITOK <tok> example.

although TEITOK originally worked in much the same way as CQPWeb rendering CQP results in the browser, it now works in a somewhat more involved way: the CQP corpus is created by a dedicated tool called tt-cwb-encode, which like cwb-encode builds CQP corpus files, except that it builds them directly from the XML files.


But doing so is very slow and labour intensive.

in the CQP corpus, both the form written by the student and the corrected form provided by the teacher are searchable, making Figure 5: A spoken document from the COPLE project it possible to search for various types of orthographic errors.


This because the reality of manuscripts can become rather complicated: in a Ladino corpus currently being developed in TEITOK, there is the original orthography, and expanded form, and the normalized form in current spelling.

when various orthographic forms are exported, say the original as well as the normalized orthography, it becomes possible to use CQP to search for orthographic changes or errors (depending on the corpus), for instance one can study the development of the word-initial h in Spanish by searching for all words that used to be written with an h but no longer are.


When various orthographic forms are exported, say the original as well as the normalized orthography, it becomes possible to use CQP to search for orthographic changes or errors (depending on the corpus), for instance one can study the development of the word-initial h in Spanish by searching for all words that used to be written with an h but no longer are.

since the orignal documents are mostly in Hebrew characters, it is very useful for acces-<w> <choice> <org>ob-<lb/>scuras</org> <reg>oscuras</reg> </choice> </w> Figure 1: A TEI <choice> example.


An interesting symptom of this is that the T E X engine is conventionally described as a living organism (Knuth, 1984, chapter 7) with eyes, mouth and stomach, rather than in terms of compiler theory.

many commonly used commands crucially depend on these functionalities.


• Many representation schemes have been designed with a particular language (or language family) in mind, which hinders its adoption for languages with different morphosyntactic structures.

application developers face many challenges when combining NLP modules, which are often tailored to the particular needs of the task at hand and, thus, difficult to integrate.


It was found that all the repetitions in them were reduced into a single letter.

if there are some remaining tokens without tags at the end of the pipeline, they get assigned the final encoding of the output text.


For every character found in a word classification in the I(0)BES scheme will be collected and frequencies will be counted for I,B,E and S respectively.

previous research on automated digitization focused on producing 3D scans of tablets (Sect.


As a logosyllabic writing system, it shares important structural characteristics with Chinese and Japanese (Ikeda, 2007), so that we evaluate word segmentation methods successfully applied to these languages.

non-restorable characters were ignored and thus are not represented in the resulting texts.


Additional information can be found such as intensity marker (e.g., severe back pain) or frequency marker (e.g., chronic back pain).

human annotators can focus on unseen annotations.


This is a typical annotated segment based on MAP, where only the lexical-entry segments are explicitly annotated with semantic structures and the semantic structures of larger segments are implicitly derived by the above rules.

speaking, an eq link represents the coreference between the head nodes of the two linked segments.


We address this question by generating artificial data with the methods described in Section 3.

in Dalli and Wilks (2006) researchers train a classifier to predict the publication date of texts within a time span of nine years.


A gender-specific first-person expression is a firstperson expression which is specific to men or women due to grammatical constraints or requirements (usually related to the grammatical gender).

the task of automatic gender attribution has been considered in computational linguistics as well.


A regexp (matching time stamps and greetings) was handcrafted to isolate posts and only posts containing gender-specific first-person expressions were kept.

erotic or pornographic fiction was marked in this category.


Table 4: Percentage of annotations assigned per category in the Multi-CoreSC CRA corpus.

for this purpose, we used the trained Conditional Random field (CRf) model (see (Liakata and others, 2012)) to automatically assign CoreSC concepts to the CRA corpus and evaluated the results on the gold standard.


We note here that this procedure has three shortcomings: that we potentially bias the gold-standard towards (i) certain CoreSC labels through the use of our prioritisation table, (ii) the most reliable annotator and (iii) that the measure is conservative and favours singlelabel annotations.

in the case of three distinct labels the top three most popular labels are assigned scores of 0.6, 0.3 and 0.1 respectively.


For this purpose, we used the trained Conditional Random Field (CRF) model (see (Liakata and others, 2012)) to automatically assign CoreSC concepts to the CRA corpus and evaluated the results on the gold standard.

this was not followed strictly by all annotators leading to some variation in the number of annotated sentences across the different annotators.


In this case, the perceiver is considering "could the agent have been forced to carry out an action" or "was the agent tricked into carrying out an action".

to the best of our knowledge, this is the first piece of work exploring an automated approach for blame/praise detection from text.


Wikipedia has become one of the most popular resources in natural language processing and it is used in quantities of applications.

this is translated in SQL as: sqlContext.sql(""" SELECt uri FROM wikidata WHERE predicate = 'wd:P31' AND value1 = 'urn:wikidata:Q5' """).cache().registertemptable ("persons") and results in a table called persons.


Colloquial creole languages such as Spanglish (a mix of Spanish and American English) or Singlish (English-based creole from Singapore) or even official languages such as the Haitian creole (which merges Portuguese, Spanish, Taíno, and West African languages), are some of the bestknown situations of spoken code-switching.

table 4 shows the accuracy obtained on   table 5: Micro-averaged F1 (%) on the code-switching set the code-switching collection by: (1) the English monolingual model (en) , (2) the Spanish monolingual model (es) and the multilingual model (en-es).


The average agreement between the annotators is fair and the average agreement between the annotators and the curator is moderate.

wordNet) or a flat hierarchy of topical groupings (cf.


For example, in the context of calculating available seats in the room, loveseat and sofa are not synonymous, as they by usual definition have a different number of seats.

we further assume that it most representatively shows the gaps in this kind of knowledge base.


We want to know the probability p(x, y) that x is the translation of y. where Improvements of the method were later proposed.

it is a widely used corpus in cross-language text analysis and machine translation.


Papers in PDF format corresponding to the most relevant search results were downloaded.

alignment of our dataset at both sentence-and chunk-level was also needed in order to evaluate the performance of different methods on different types of texts but also on different sizes of texts.


The annotation is still ongoing and the resource continues to grow.

we do achieve a rate of 73.9% of text units with code-switching (see below), which contrasts with code-switching ratios of around 20% in data sets in previous literature (Solorio et al., 2014).


(2013), such that all the evaluation scripts used in the SemEval 2013 CL-WSD task can also be used on this data.

there is an expression like "melting pot", which is not in the dictionaries.


The enrichment is supported via a query template mechanism.

resource: /e-entity/{ner-engine}/documents Input: Berlin is the capital of Germany.


We use a weighted arithmetic score combination for LSA, PCA or ICA as they are based on the same similarity measure (Normalized Euclidean distance) and a weighted arithmetic ranks combination while using SA in the combination process (SA is based on Jaccard similarity).

we notice that SA performs better than  the three other models for the breast cancer corpus.


We provide the results and code for a number of comparisons and benchmarks that test the usability of this toolkit against other toolkits in the literature.

c(king) − c(man) ≈ c(queen) − c(woman), corresponding to some sort of royalty concept.


[2003]'s neural language model, with a number of tricks to boost performance.

for each update in word2vec, bivec performs 4 updates: source to source, source to target, target to target and target to source.


A follow up project, TaaS 2 , expanded this idea to all EU languages.

note we ensure that the time difference between those two bins is no greater than 7 days.


Corpora of original and translated language are essential for empirical investigation of theoretically-motivated hypotheses from the field of translation studies (Baker, 1996).

in each subcorpus, each paragraph is annotated with meta-information; in particular, the original language in which the paragraph was uttered.


Do note that since we added beginning markers ('%') and end markers ('# ') to words, our rules allow to explicitly distinguish between initial and final positions within the words, which lends itself well to the inflectional schemes of the Slavic languages under consideration.

polish uses ks instead of x: CS-pL maximum -maksymum, export -eksport ((x:ks) is identified in iteration 190).


This explains the variation in the amount of words in Table 1   The words in the lists belong to different parts of speech.

although the Czech internationalisms use the letter combinations ti and di, t and d are not palatalized by the i as they CRITIC parts in characters with diacritics, with the hypothesis that differences in BaSE would be stronger than differences in DIa-CRITIC.


In both cases, one deals with certain ensembles of units (Akhmanova, 1971).

in the CS-PL cognate pair večer -wieczór 'evening', the model suggests the correspondences (r:r), (e:e), (e:ie),  While the model does not replicate all of the diachronicallybased rules, not all of them are necessarily truly applicable.


Furthermore, there are no exceptions for internationalisms in Polish orthography, in contrast to Czech.

we assume that Russian verbs of the first or the second regular conjugations may correspond to Bulgarian verbs of the first, the second or the third regular conjugations.


Like us, they provide metaphors covering a variety of syntactic relationships for English, Spanish, Russian, and Farsi.

metaphors with higher metaphoricity are expected to exhibit a higher degree of the features above, whereas metaphors with lower metaphoricity are expected to exhibit lower sensory perceptibility and to be less expressive, more common, and less jarring when read and interpreted literally.


These data falls under one of the following categories: 2) Annotator-Units assignment: including information about each unit (post, tweet) assigned to the annotators such as post-id, genre-id, assignment-id, path of the assigned file; Finally 3) Language-Unit assignment: which includes information about which unit belongs to which dialect/language.

the administrator manages only one central database.


Multilingual corpora, combined with alignment and search tools, are today acknowledged for their theoretical as well as practical importance in cross-linguistic studies and applications: they provide a rich basis of language correspondences in context that can serve as testbeds for linguistic theories and hypotheses, but they are also essential for applications in the fields of lexicography, natural language processing, automatic or machine-assisted translation and language teaching (Altenberg and Granger, 2002;Johansson, 2007).

an ID Gloss and a [begin,end] time interval) to the corresponding part of the French translation fragment where the meaning of this sign is given in context.


A subeffect test showed significant simple main effect of language in Grounding act "init" (F(1, 99) = 4.53, p < .05), "ack-init" (F(1, 99) = 8.91, p < .01) "cont" (F(1, 99) = 50.84, p < .001).

three sets of NAC EMR-9 head-mounted eye trackers and headsets with microphones recorded their eye gazes and voices.


Additionally, unexposed broadcast data from prior LDC collection efforts contributed additional recordings for Indian English, Mandarin, Modern Standard Arabic and US English.

to create the CtS portion of the corpus, we recruited native speakers following the same approach used for LRE11, in which recruited callers (known as "claques") were required to make single calls to multiple unique individuals within their established social networks (friends, family, acquaintances).


A massive, but expected, drop compared to the 96.3% on the newswire WSJ corpus.

in our FlexTag architecture (see Figure 1d), we thus give the user flexible control over which feature extraction and machine learning should be used.


Training FlexTag Most other trainable taggers only support one input format and users are supposed to transform their data in the required format.

in order to make this simplicity possible, FlexTag relies on DKPro Core 1 (Eckart de Castilho and Gurevych, 2014) for preprocessing and model loading, and DKPro TC (Daxenberger et al., 2014) for feature extraction and classification.


In 2015 we set the bar to 500 thousand tokens, mostly because of our results on morphosyntactic annotation of Slovene (Ljubešić and Erjavec, 2016) which showed corpus supervision to be of much greater importance than lexicon supervision.

croatian lijep, Serbian lep) which was already encoded during the croatian annotation.


One training epoch on a tagged corpora of average size (15-50 thousand sentences) runs for 15-90 minutes on a regular 2-core CPU, and it usually took 15-20 epochs until converged.

table 1 shows our results on this dataset, compared to those reported in (Collobert et al., 2011).


], NN [=Noun], VB [=Verb] or PR [=Pronoun], and additional features, e. g. Number (Sg.

this phenomenon makes checking for congruence difficult without a morphological component.


Here are some examples: In the sentence Libri u botua., engl.


In February 2016, Amazigh language was made an official language by the Algerian government.

finally, in Section 6 we draw some conclusions and describe the work to be done in the near future.


These unsupervised or semisupervised approaches make use of distributional semantics (Turian et al., 2010).

data deviating from the norm requires tools adjusted to this data.


To make sure that the batch size of 200 sentences is not too big, we experiment with 100, 50 and 1.

word embeddings have been used by Lin et al.


We perform an initial automated POS tagging on the corpus.

each occurrence was marked with its sense tag.


This issue is somewhat similar to that of compound words in Swedish (Heppin and Gronostaj, 2012).

instead of adding an extra annotation layer 5 , like in the SweFN project, we decided to followed Petruck's recommendation; borrowing from the Spanish FrameNet project, we annotate such roles as "externally constructionally null instantiated" (ECNI) (Subirats, 2009).


This is a specific feat of digital newspaper collections, where users often search for news items relating either to their own family history or a location that they live(d) in or have some other personal relation to, and which are of particular interest to genealogists.

this can be due to the annotators accidentally missing an entity (only 1 run was made per page) or, some of the existing annotations having been scraped during post-processing.


For each occurrence of a T C, the expert should answer if: (1) it is syntactically well-formed, (2) it belongs to the scientific lexicon (3) it belongs to the domain lexicon (here linguistics), (4) it is a T O.

among these association rules, let us give a focus on :  -NTO] means that when Sa gives a N T O diagnosis when HB and LS cannot take decision, then it is not always a good diagnosis (confidence is only 0.7).


Potential terms are n-grams, mainly unigrams, occurring in Swedish patent texts.

n T Os are more equally Figure 1: Decision Tree computed for occurrences of the T C Aspect: each node is an XML tag and each edge exhibits the normalized distance between on occurrence and the closest tag of this type, for each decision (T O or n T O), the proportion of True Positives is given distributed within the document.


In Spanish, poetic lines are measured in syllables.

the system consists of four main modules (see Figure  1): 1.


Let the prominence estimate P r(r i ) be the relative frequency with which the resource r i appears linked on Wikipedia compared to the frequency of all other resources in R. Formally: We estimate entity prominence through PageRank (Page et al., 1999).

we hope that this work may foster metric-aware and less biased benchmark datasets to be created, as well as it may stimulate a more sensitive discussion of results produced using those benchmarks.


First, the number of additional atoms that need to be added to the network can be large depending on the domain size of the predicate's arguments.

the system configurations underlying the three rows in this table are the same as those in table 2.


the ability to learn from data is one of the advantages of MLNs over ILP: unlike MLNs, ILP is a pure inference framework that does not have the ability to learn.


In this subsection, we provide a brief overview of two joint inference frameworks, ILP and MLNs.

an opinion candidate can be related to source and target arguments if it is not an argument-implicit opinion.


To verify the usefulness of our corpora we built machine-learning models using an exhaustive list of features, and while utilizing the annotated corpora for training and testing.

we collected at least 4 annotations from different annotators for each blog.


one of the assumptions is that domain experts are knowledgeable about the characteristics of the phenomena that they would like to be captured by the corpus.


The point is not to find all coreferences in advance or we would not need to make the tool, but to insure enough variance in the documents to produce a subnetwork representing the coreferences adequately.

other optimisation methods could be easily incorporated.


The first three verbs were selected because they are often referred to as typical reported speech verbs, while the other three were typical cases of describing other speech properties, as shown in the examples below, lightly adapted from COMPARA: • A Jean contouà Betty e a Betty contou-me.

drury and colleagues compiled a large quotation corpus in the financial domain (drury et al., 2011;drury and Almeida, 2012) and used it to identify trends in that domain.


In a time of extremely short-lived fame, "who said that" is mostly relevant if it is modified by, or served as "has just said so", and one needs automatic reporting boots.

in their study of human tagging in English, (Bruce and Wiebe, 1999) already showed that attribution was hard, and in (Wiebe et al., 2003) the problem of automated opinion mining is further discussed.


New contexts for the exploration of reported speech have recently been provided by research in Digital Humanities, see (Mambrini et al., 2012).

3 it should be stressed that our interest is also linguistic, in the sense that we wish to define and study a lexical field, that of language-talk-speech, that contains the words related to this (central) property of mankind.


The First CLIN Dutch Shared Task at CLIN26 was based on the Dutch section, while the EVALITA 2016 FactA (Event Factuality Annotation) shared task, based on the Italian section, is currently being organized.

previous work is based on the use of corpora aligned at the token level, whereas our method envisages an alignment at the markable level, where each annotated element is aligned to English on a semantic rather than syntactic basis.


The focus of visual attention is one such behavior and we wish to gain deeper understanding of how it is effected and explore patterns it exhibits under different interaction conditions.

spatial alignment is done on several levels as well.


humans listening to speech can miss or imagine the existence of objectively measured silences of short duration, especially when there is elongation of previous or following syllables (Martin, 1970), and are known to have difficulty recalling disfluencies from audio they have heard (Deese, 1980).


Secondly, given that an average user is not even aware of this processing taking place, it is practically impossible for him to exercise rights that are granted to him by the Directive, such as the right of access (art.

the first case -processing one's own data -does not seem to raise any particular concerns as far as the lawfulness is concerned.


a person who processes data on behalf of the controller).

shannon's work on Information Theory to treat translation as a code-breaking problem (Hutchins, 1999).


People have already developed some initial applications; but they have to develop everything by themselves.

intra-Grid Executor as well as inter-Grid Executor are played the important role in our framework.


The use of mobile devices to assist people in overcoming language barriers is becoming more and more popular such as translation via an image of unknown word, the spoken word, and gestures.

our proposed framework includes appropriate modules to deal with those issues.


When the user clicks on a category (e.g.

b) The set of tags adopted should be interpretable across linguistic theories and valid across time in order to last as long as possible as old linguistic atlases did.


1 For these reasons, the methodological and technological boundaries existing in each linguistic project need to be overcome in order to find common grounds where linguistic material can be shared and re-used over a long period of time.

the FLaReNet Strategic Agenda highlighted the most pressing needs for research areas, such as Natural Language Processing, and presented a set of recommendations for the development and progress of LRs in Europe (Soria et al., 2014).


10 See for example the overview of the different NIST evaluations on the Linguistic Data Consortium (LDC) website https://www.ldc.upenn.edu/collaborations/ evaluations/nist.

the development of a language 23 See https://bitbucket.org/dcavar/ elan2split for more details.


By the time of this writing, they have been implemented and tested on small data sets but not on the actual large data sets.

as a workaround, corpora are sometimes distributed as sentence shuffles under the assumption that single sentences never reach the required threshold of creativity, for example COW (Schäfer and Bildhauer, 2012;Schäfer, 2015b) or the Leipzig Corpora Collection (Biemann et al., 2007).


A potential difference of the reviewed approaches lies within the flexibility that is offered to the users with respect to the level of customisation of the annotation schemas.

the graphical interface of the annotation page is divided into main three parts, as shown in Figure 4.


The main difference between these annotation tools, is that WebAnno separates the role "Curator" as the user who reviews, merges the annotations of the different users and produces the final result.

the proposed annotation tool exploits the power of cloud computing in order to allow users to perform annotation tasks without the need of any kind of software.


In batch processing all the cluster nodes are busy processing a large set of documents, whereas in a streaming processing scenario resources may be idle, waiting for documents to arrive.

depending on the requirements and quantity of the data this can be done in different ways.